Implementation Of An Air Supply Unit Control Scheme For The Uc2av (Unmanned Circulation Control Aerial Vehicle),
2016
University of Denver
Implementation Of An Air Supply Unit Control Scheme For The Uc2av (Unmanned Circulation Control Aerial Vehicle), Cameron Rosen
Electronic Theses and Dissertations
The expanded prevalence of Unmanned Aerial Vehicles (UAVs) in recent years has created many opportunities to research novel applications for their use, enabled by the reduced cost, mission flexibility, and reduced risk that small-scale unmanned platforms provide in comparison to larger aircraft. Despite the versatility of unmanned aviation, limitations on payload size and weight, fuel and power capacity, and takeoff and landing infrastructure can restrict UAV applications, and have created a need for lift augmenting technologies that can reduce the impact of these limitations. Circulation Control (CC) is an active flow technique that has been proven as a method for …
Automated Design Of Boolean Satisfiability Solvers Employing Evolutionary Computation,
2016
Missouri University of Science and Technology
Automated Design Of Boolean Satisfiability Solvers Employing Evolutionary Computation, Alex Raymond Bertels
Masters Theses
"Modern society gives rise to complex problems which sometimes lend themselves to being transformed into Boolean satisfiability (SAT) decision problems; this thesis presents an example from the program understanding domain. Current conflict-driven clause learning (CDCL) SAT solvers employ all-purpose heuristics for making decisions when finding truth assignments for arbitrary logical expressions called SAT instances. The instances derived from a particular problem class exhibit a unique underlying structure which impacts a solver's effectiveness. Thus, tailoring the solver heuristics to a particular problem class can significantly enhance the solver's performance; however, manual specialization is very labor intensive. Automated development may apply hyper-heuristics …
Enabling Carrier Collaboration Via Order Sharing Double Auction: A Singapore Urban Logistics Perspective,
2016
Singapore Management University
Enabling Carrier Collaboration Via Order Sharing Double Auction: A Singapore Urban Logistics Perspective, Handoko Stephanus Daniel, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
A recent exploratory study on the collaborative urban logistics in Singapore suggests that cost reduction and privacy preservation are two main drivers that would motivate the participation of carriers in consolidating their last mile deliveries. With Singapore's mild restrictions on the vehicle types or the time windows for the last-mile delivery, we believe that with proper technology in place, an Urban Consolidation Center like the Tenjin Joint Distribution System in Fukuoka Japan may be implemented to achieve cost reduction with some degree of privacy preservation. Participating carriers keep their respective private orders and have the option to get their remaining …
An Extended Study On Addressing Defender Teamwork While Accounting For Uncertainty In Attacker Defender Games Using Iterative Dec-Mdps,
2016
Singapore Management University
An Extended Study On Addressing Defender Teamwork While Accounting For Uncertainty In Attacker Defender Games Using Iterative Dec-Mdps, Eric Shieh, Albert Xin Jiang, Amulya Yadav, Pradeep Varakantham, Milind Tambe
Research Collection School Of Computing and Information Systems
Multi-agent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multi-agent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork research in security games. The problem that this paper seeks to solve is the coordination of decentralized defender agents in the presence of uncertainty while securing targets against an observing adversary. To address this problem, we offer the following novel contributions in this paper: (i) New model of security games with defender teams that coordinate under uncertainty; (ii) New algorithm based on column …
Representation And Analysis Of Multi-Modal, Nonuniform Time Series Data: An Application To Survival Prognosis Of Oncology Patients In An Outpatient Setting,
2016
Michigan Technological University
Representation And Analysis Of Multi-Modal, Nonuniform Time Series Data: An Application To Survival Prognosis Of Oncology Patients In An Outpatient Setting, Jennifer Winikus
Dissertations, Master's Theses and Master's Reports
The representation of nonuniform, multi-modal, time-limited time series data is complex and explored through the use of discrete representation, dimensionality reduction with segmentation based techniques, and with behavioral representation approaches. These explorations are done with a focus on an outpatient oncology setting with the classification and regression analysis being used for length of survival prognosis. Each decision of representation and analysis is not independent, with implications of each decision in method for how the data is represented and then which analysis technique is used. One unique aspect of the work is the use of outpatient clinical data for patients, which …
Using Topic Modelling Algorithms For Hierarchical Activity Discovery,
2016
Technological University Dublin
Using Topic Modelling Algorithms For Hierarchical Activity Discovery, Eoin Rogers, John D. Kelleher, Robert J. Ross
Conference papers
Activity discovery is the unsupervised process of discovering patterns in data produced from sensor networks that are monitoring the behaviour of human subjects. Improvements in activity discovery may simplify the training of activity recognition models by enabling the automated annotation of datasets and also the construction of systems that can detect and highlight deviations from normal behaviour. With this in mind, we propose an approach to activity discovery based on topic modelling techniques, and evaluate it on a dataset that mimics complex, interleaved sensor data in the real world. We also propose a means for discovering hierarchies of aggregated activities …
Improving The Vector Auto Regression Technique For Time-Series Link Prediction By Using Support Vector Machine,
2016
Ateneo de Manila University
Improving The Vector Auto Regression Technique For Time-Series Link Prediction By Using Support Vector Machine, Proceso L. Fernandez Jr, Jan Miles Co
Department of Information Systems & Computer Science Faculty Publications
Predicting links between the nodes of a graph has become an important Data Mining task because of its direct applications to biology, social networking, communication surveillance, and other domains. Recent literature in time-series link prediction has shown that the Vector Auto Regression (VAR) technique is one of the most accurate for this problem. In this study, we apply Support Vector Machine (SVM) to improve the VAR technique that uses an unweighted adjacency matrix along with 5 matrices: Common Neighbor (CN), Adamic-Adar (AA), Jaccard’s Coefficient (JC), Preferential Attachment (PA), and Research Allocation Index (RA). A DBLP dataset covering the years from …
An Improved Smote Algorithm Based On Genetic Algorithm For Imbalanced Data Collection,
2016
Hubei University
An Improved Smote Algorithm Based On Genetic Algorithm For Imbalanced Data Collection, Qiong Gu, Xian-Ming Wang, Zhao Wu, Bing Ning, Chun-Sheng Xin
Electrical & Computer Engineering Faculty Publications
Classification of imbalanced data has been recognized as a crucial problem in machine learning and data mining. In an imbalanced dataset, minority class instances are likely to be misclassified. When the synthetic minority over-sampling technique (SMOTE) is applied in imbalanced dataset classification, the same sampling rate is set for all samples of the minority class in the process of synthesizing new samples, this scenario involves blindness. To overcome this problem, an improved SMOTE algorithm based on genetic algorithm (GA), namely, GASMOTE was proposed. First, GASMOTE set different sampling rates for different minority class samples. A combination of the sampling rates …
Integrating Cobots In Engineering Technology Education,
2016
Wayne State University
Integrating Cobots In Engineering Technology Education, Ana M. Djuric, Vukica Jovanovic, Tatiana V. Goris, Otilia Popescu
Engineering Technology Faculty Publications
Collaborative robots or CoBots, unlike traditional robots, are safe and flexible enough to work harmoniously with humans. Exploiting the efficiency of automated operations and the flexibility of manual operations in one process can improve productivity and worker job satisfaction. CoBots technology has been experiencing strong growth in different areas such as ground transportation, food-processing industry, car manufacturing, and naval or aeronautical engineering. Current CoBots education and training opportunities are rare or non-existent in university environments. In response to this need, we developed several CoBots modules which will be integrated in the current robotics and mechatronics courses. In this paper we …
The Reconfigurable Machinery Efficient Workspace Analysis Based On The Twist Angles,
2016
Old Dominion University
The Reconfigurable Machinery Efficient Workspace Analysis Based On The Twist Angles, Ana M. Djuric, Vukica Jovanovic, Mirjana Filipovic, Ljubinko Kevac
Engineering Technology Faculty Publications
A novel methodology for the calculation, visualisation and analysis of the Reconfigurable Machinery Efficient Workspace (RMEW), based on the twist angles, is presented in this paper. The machinery's kinematic parameters are used for calculating the workspace, while the efficient workspace is associated with the machinery's path and includes the end-effector position and orientation. To analyse and visualise many different machinery efficient workspaces at the same time, the calculation is based on the previously developed and validated complex reconfigurable machinery's kinematic structure named n-DOF Global Kinematic Model (n-GKM). An industrial robot is used as an example to demonstrate …
Applications Of Computational Geometry And Computer Vision,
2016
Central Washington University
Applications Of Computational Geometry And Computer Vision, Joseph Lemley
All Master's Theses
Recent advances in machine learning research promise to bring us closer to the original goals of artificial intelligence. Spurred by recent innovations in low-cost, specialized hardware and incremental refinements in machine learning algorithms, machine learning is revolutionizing entire industries. Perhaps the biggest beneficiary of this progress has been the field of computer vision. Within the domains of computational geometry and computer vision are two problems: Finding large, interesting holes in high dimensional data, and locating and automatically classifying facial features from images. State of the art methods for facial feature classification are compared and new methods for finding empty hyper-rectangles …
Using Genetic Algorithms To Evolve Artificial Neural Networks,
2016
Colby College
Using Genetic Algorithms To Evolve Artificial Neural Networks, William T. Kearney
Honors Theses
This paper demonstrates that neuroevolution is an effective method to determine an optimal neural network topology. I provide an overview of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, and describe how unique characteristics of this algorithm solve various problem inherent to neuroevolution (namely the competing conventions problem and the challenges associated with protecting topological innovation). Parallelization is shown to greatly speed up efficiency, further reinforcing neuroevolution as a potential alternative to traditional backpropagation. I also demonstrate that appropriate parameter selection is critical in order to efficiently converge to an optimal topology. Lastly, I produce an example solution to a medical …
Mining Human Activity Using Dimensionality Reduction And Pattern Recognition,
2016
Mohammed V University
Mining Human Activity Using Dimensionality Reduction And Pattern Recognition, Ismail El Moudden, Mounir Ouzir, Badreddine Benyacoub, Souad El Bernoussi
Research and Infrastructure Service Enterprise (RISE) Faculty Publications
Human activity recognition (HAR) is an emerging research topic in pattern recognition, especially in computer vision. The main objective of human activity recognition is to automatically detect and analyze human activities from the information acquired from different sensors. Human activity prediction using big data remains a challengingly open problem. Several approaches have recently been developed in order to find practical ways to solve high dimensionality of data problems. The aim of this study is to attempt, using data mining techniques, to deal with HAR modeling involving a significant number of variables in order to identify relevant parameters from data and …
Pattern Discovery In Dna Using Stochastic Automata,
2015
San Jose State University
Pattern Discovery In Dna Using Stochastic Automata, Shweta Shweta
Master's Projects
We consider the problem of identifying similarities between different species of DNA. To do this we infer a stochastic finite automata from a given training data and compare it with a test data. The training and test data consist of DNA sequence of different species. Our method first identifies sentences in DNA. To identify sentences we read DNA sequence one character at a time, 3 characters form a codon and codons form proteins (also known as amino acid chains).Each amino acid in proteins belongs to a group. In total we have 5 groups’ polar, non-polar, acidic, basic and stop codons. …
Applying Bayesian Machine Learning Methods To Theoretical Surface Science,
2015
Washington University in St. Louis
Applying Bayesian Machine Learning Methods To Theoretical Surface Science, Shane Carr
McKelvey School of Engineering Graduate Student Theses & Dissertations
Machine learning is a rapidly evolving field in computer science with increasingly many applications to other domains. In this thesis, I present a Bayesian machine learning approach to solving a problem in theoretical surface science: calculating the preferred active site on a catalyst surface for a given adsorbate molecule. I formulate the problem as a low-dimensional objective function. I show how the objective function can be approximated into a certain confidence interval using just one iteration of the self-consistent field (SCF) loop in density functional theory (DFT). I then use Bayesian optimization to perform a global search for the solution. …
Predicting Energy Demand Peak Using M5 Model Trees,
2015
Western University
Predicting Energy Demand Peak Using M5 Model Trees, Sara S. Abdelkader, Katarina Grolinger, Miriam Am Capretz
Electrical and Computer Engineering Publications
Predicting energy demand peak is a key factor for reducing energy demand and electricity bills for commercial customers. Features influencing energy demand are many and complex, such as occupant behaviours and temperature. Feature selection can decrease prediction model complexity without sacrificing performance. In this paper, features were selected based on their multiple linear regression correlation coefficients. This paper discusses the capabilities of M5 model trees in energy demand prediction for commercial buildings. M5 model trees are similar to regression trees; however they are more suitable for continuous prediction problems. The M5 model tree prediction was developed based on a selected …
Battle Bot Ai – Patriot Bot,
2015
California Polytechnic State University, San Luis Obispo
Battle Bot Ai – Patriot Bot, James Johnston
Computer Engineering
An entry in the the 'Battle Block AI' competition hosted by 'The AI Games'.
Energy Forecasting For Event Venues: Big Data And Prediction Accuracy,
2015
Western University
Energy Forecasting For Event Venues: Big Data And Prediction Accuracy, Katarina Grolinger, Alexandra L'Heureux, Miriam Am Capretz, Luke Seewald
Electrical and Computer Engineering Publications
Advances in sensor technologies and the proliferation of smart meters have resulted in an explosion of energy-related data sets. These Big Data have created opportunities for development of new energy services and a promise of better energy management and conservation. Sensor-based energy forecasting has been researched in the context of office buildings, schools, and residential buildings. This paper investigates sensor-based forecasting in the context of event-organizing venues, which present an especially difficult scenario due to large variations in consumption caused by the hosted events. Moreover, the significance of the data set size, specifically the impact of temporal granularity, on energy …
A Layered Hidden Markov Model For Predicting Human Trajectories In A Multi-Floor Building,
2015
Singapore Management University
A Layered Hidden Markov Model For Predicting Human Trajectories In A Multi-Floor Building, Qian Li, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Tracking and modeling huge amount of users’ movement in a multi-floor building by using wireless devices is a challenging task, due to crowd movement complexity and signal sensing accuracy. In this paper, we use Layered Hidden Markov Model (LHMM) to fit the spatial-temporal trajectories (with large number of missing values). We decompose the problem into distinct layers that Hidden Markov Models (HMMs) are operated at different spatial granularities separately. Baum-Welch algorithm and Viterbi algorithm are used for finding the probable location sequences at each layer. By measuring the predicted result of trajectories, we compared the predicted results of both single …
Oriented Object Proposals,
2015
Singapore Management University
Oriented Object Proposals, Shengfeng He, Rynson W. H. Lau
Research Collection School Of Computing and Information Systems
In this paper, we propose a new approach to generate oriented object proposals (OOPs) to reduce the detection error caused by various orientations of the object. To this end, we propose to efficiently locate object regions according to pixelwise object probability, rather than measuring the objectness from a set of sampled windows. We formulate the proposal generation problem as a generative probabilistic model such that object proposals of different shapes (i.e., sizes and orientations) can be produced by locating the local maximum likelihoods. The new approach has three main advantages. First, it helps the object detector handle objects of different …
